National Natural Science Foundation of China (NSFC)
82122064
China
Citation
Journal: Acta Pharm Sin B / Year: 2026 Title: Deep learning-driven discovery and mechanism of action study of a minimalist conopeptide targeting 7 nicotinic acetylcholine receptor. Authors: Jinghui Zhang / Zhengji Yin / Yue Li / Cheng Ge / Zixuan Zhang / Pu Yuan / Tao Jiang / David J Craik / Yan Zhao / Rilei Yu / Abstract: Despite extensive structural and functional characterization of the 7 nicotinic acetylcholine receptor, valuable structural insights into its interactions with conopeptides remain limited, thereby ...Despite extensive structural and functional characterization of the 7 nicotinic acetylcholine receptor, valuable structural insights into its interactions with conopeptides remain limited, thereby hindering the rational development of peptide-based modulators for this clinically important receptor subtype. Here, we present an integrated pipeline combining deep learning, structural biology, computational modeling and electrophysiology to accelerate the discovery and optimization of 7 nAChR-targeting conopeptides. To overcome data scarcity, we developed a deep learning model using the ESM-2 protein language framework, enabling efficient screening of 689 disulfide-poor conopeptides. This approach identified SS1, a novel antagonist of 7 nAChR, which was systematically optimized structure-activity relationship studies to yield [ΔQP,S8R]SS1-a minimalist peptide with nanomolar potency (IC = 49.2 nmol/L), enhanced selectivity, and improved stability. Cryo-EM and computational modeling resolved the 3.3 Å resolution structure of 7 nAChR bound to [S8R]SS1, revealing a unique binding mode stabilized by hydrogen bonds, hydrophobic interactions, and glycan contacts, while hybrid receptor conformations (closed/desensitized) elucidated its inhibitory mechanism. This work establishes a transformative deep learning-to-experiment framework for accelerating the discovery and optimization of nature-inspired peptide therapeutics.
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